Water Quality Modeling of Al-Qilt Stream Shraideh_0.pdf · Chapter One: Introduction 1 1.1 General...

An-Najah National University

Faculty of Graduate Studies

Water Quality Modeling of Al-Qilt

Stream

By

Hani Adel Shraideh

Supervisor

Dr. Abdel Fattah Hasan

Co-Supervisor

Dr. Sameer Shadeed

This Thesis is Submitted in Partial Fulfillment of the Requirements for

The Degree of Master of Water Environmental Engineering, Faculty of

Graduate Studies, An-Najah National University, Nablus, Palestine. 2014

III

Dedication

Rising endless thanking to Allah for his uncountable blessing and guidance

and mercy on me.

May Allah put peace and blessing on our prophet Mohammad and his

saintly family and his elite superior companions.

All the credit and favor is due to our merciful Allah and my soulful parents

who overwhelmed me with care kindness tender and endless support, I

want to thank my mother and my father for believing in me, and for being

the truly friends and companions in this journey.

Mother, I dedicate my soul for you. Mother, I dedicate my life for you.

Mother, I dedicate my existence for you.

Father, you are the one who sculpted me with your own hands. May Allah

gives me the strength to be dutiful son for you.

My brothers (Ayman & Ibraheem) and sisters (Dyana & Manal) thank you

for being a great source of support and encouragement.

My supervisors Dr. Abdel Fattah Hasan and Dr. Sameer Shadeed thank you

for your support, patience and motivation to make my thesis the best

possible.

My friend Mohammad Homaidan, I couldn’t do this without you, thank

you so much.

My colleagues in the Palestinian Water Authority, thank you for your

support and help in the laboratory tests.

IV

Acknowledgement

I would like to express my sincere gratitude to Dr. Abdel Fattah Hasan and

Dr. Sameer Shadeed for their supervision, continuous support of my study,

patience, motivation, enthusiasm, and immense knowledge. Their guidance

helped me all the time on working and writing my research to make this

thesis the best possible.

I owe my deepest gratitude to Dr. Subhi Samhan for supporting my thesis,

helping me in providing data and giving me fruitful suggestions.

I thank Palestinian Water Authority (PWA) and SMART project for

funding 50% of the tuition fees for the final year of my master’s study and

helping me in providing the needed data. Special thanks go to Ala’ Al

Masri, Ghaleb Bader, Majeda Alawneh, Hanadi Bader, and Ashraf

Dweikat.

I am grateful to my friend Jareh Hasan who helped me the first time I

collected water samples.

I am grateful to my family and friends for their support love and care they

gave to me in my life.

My sincere appreciation goes to all those who have assisted me and have

not been mentioned. Thank you.

V

اإلقزار

الرسالة التي تحمل العنوان:أنا الموقع أدناه مقدم

Water Quality Modeling of Al-Qilt Stream

إليرو تمرت اششرارة مرا باسرتننا الخرا، جيردي نتراج ىرو إنمرا الرسرالة ىرهه عميرو اشرتممت مرا برنن اقرر

بحني أو عممي بحث أو درجة أية لنيل قبل من جز منيا لم يقدم أو ككل الرسالة ىهه وان ورد حينما

أخرى . بحنية أو تعميمية مؤسسة أية لدى

Declaration

The work provided in this thesis, unless otherwise referenced, is the

researcher's own work, and has not been submitted elsewhere for any other

degree or qualification.

:Student's name اسم الطالب:

:Signature التوقيع:

:Date التاريخ:

VI

Table of Contents No. Subject Page

Dedication III

Acknowledgments IV

Declaration V

Table of Contents VI

List of Tables VIII

List of Figures IX

List of Abbreviations XI

Abstract XIII

Chapter One: Introduction 1

1.1 General Background 1

1.2 Problem Statement 2

1.3 Research Motivations 2

1.4 Research Objectives 3

1.5 Research Outputs 3

1.6 Thesis Outline 4

Chapter Two: Literature Review 5

2.1 Al-Qilt Streamflow Quality and Pollution 5

2.2 Fate and Transport 6

2.2.1 Dissolved Oxygen Sag Curve 9

2.3 QUAL2Kw Parameters and Theory 12

2.3.1 Segmentation and Hydraulics 14

2.3.2 Quantities 15

2.3.3 Inputs 15

2.3.4 Outputs 16

2.3.5 Key Input Parameters 16

2.4 Water Quality Models (Catchment Scale) 19

2.5 Case Studies 22

Chapter Three: Study Area 26

3.1 Geography and Topography 26

3.2 Stream Description 27

3.3 Population 28

3.4 Climate 29

3.5 Rainfall 31

3.6 Streamflow 32

Chapter Four: Methodology 34

4.1 Introduction 34

4.2 Field and Laboratory Work 35

4.2.1 Site Investigation and Characterization of the Study

Area 35

VII

4.2.2 Allocating Sampling Locations 35

4.2.3 Sampling Frequency 37

4.2.4 Samples Collection 38

4.2.5 Samples Analyses and Stream Characterization 39

4.2.5.1 Field Tests 39

4.2.5.2 Laboratory Analyses 39

4.3 Setting up The Model 39

4.3.1 Current Situation Scenario 39

4.3.2 Future Model Scenarios 39

Chapter Five: Results and Discussion 41

5.1 Introduction 41

5.2 Stream Characterization 41

5.2.1 Physical Characteristics 41

5.2.2 Field Measured Characteristics 43

5.2.3 Laboratory Characteristics 46

5.3 Modeling Scenarios 51

5.3.1 Rate Constants 51

5.3.2 Current Situation (S1) 54

5.3.3 Future Scenario (S2) 60

5.3.4 Future Scenario (S3) 63

Chapter Six: Conclusions and Recommendations 66

6.1 Conclusions 66

6.2 Recommendations 67

References 69

Appendices 76

ب الملخص

VIII

List of Tables

No. Subject Page Table (3.1) Factors used in stream survey and assessment 28

Table (3.2) Population of Palestinian communities 29

Table (3.3) Population of Israeli settlements 29

Table (3.4) Flow measures for the five sampling locations 32

Table (4.1) Sampling locations description 36

Table (5.1) Summary of physical characteristics of Al-Qilt

stream 42

Table (5.2) Dissolved Oxygen deficit between measured and

saturation concentrations 45

Table (5.3) Measured EC values for samples on April, May,

and June 2013 46

Table (5.4) BOD ranges for the five sampling locations 47

Table (5.5) Nitrogen levels for Al-Qilt stream on April, May

and June 2013 50

Table (5.6) Reaeration rate and temp. correction for Al-Qilt

stream on April, May and June 2013 52

Table (5.7) Deoxygenation rate and temp. correction for Al-Qilt


Table (5.8) BOD rate constant and temp. correction for Al-Qilt


Table (5.9) DO concentrations using weirs in the upstream

reach on April, May and June 2013 60

IX

List of Figures

No. Subject Page

Figure (2.1) Dissolved Oxygen Sag Curve. (Davis and Cornwell,

2008) 10

Figure (2.2) Turbulent diffusion of tracer particles in uniform flow.

(NOAA, 2005) 11

Figure (2.3) QUAL2Kw segmentation scheme. (QUAL2Kw Theory

and Documentation, 2008) 14

Figure (3.1) General map of Al-Qilt catchment location. 27

Figure (3.2) Average annual temperature ranges of Al-Qilt

Catchment. 30

Figure (3.3) Annual rainfall of Ramallah 31

Figure (3.4) Rainfall contour map for Al-Qilt Catchment. 32

Figure (4.1) Research methodology 34

Figure (4.2) Elevation profile of Al-Qilt stream 36

Figure (4.3) Sampling locations map 37

Figure (4.4) Collecting samples from Al-Qilt stream 38

Figure (4.5) Stepped aeration cascades 40

Figure (5.1) Gaining/Losing location sketch along the stream 42

Figure (5.2) Measured DO concentrations with associated

temperatures, on April 2013 43


temperatures, on May 2013 44


temperatures, on June 2013 44

Figure (5.5) COD for the five sampling locations on April, May,

and June 2013 49

Figure (5.6) TSS for the five sampling locations on April, May, and

June 2013 51

Figure (5.7) Simulated DO levels for current situation in the

upstream reach on April 2013 56


upstream reach on May 2013 56


upstream reach on June 2013 57


downstream reach on April 2013 58


downstream reach on May 2013 59


downstream reach on June 2013 59

Figure (5.13) Simulated DO levels for future scenario 2 in the


Figure (5.14) Simulated DO levels for future scenario 2 in the 62

X

upstream reach on May 2013






upstream reach on May 2013 64



XI

List of Abbreviations

°C Degree Centigrade

AGNPS Annualized Agriculture Non-Point Source

ARIJ Applied Research Institute/Jerusalem

ARS Agricultural Research Service

BMPs Best Management Practices

BOD5 Biochemical Oxygen Demand (after five days)

c Tracer concentration.

cm Centimeter

CBOD Carbonaceous Biochemical Oxygen Demand

COD Chemical Oxygen Demand

Cs Saturation Concentration

D Dispersion coefficient.

DBM Data-Based Mechanistic

DO Dissolved Oxygen

DOs Saturated Dissolved Oxygen

DOC/ NPOC Dissolved Organic Carbon/Nonpurgeable Organic

Carbon

EC Electric Conductivity

EF Enrichment Factor

GIS Geographical Information System

HDPE High Density Poly Ethylene

INCA Integrated Nitrogen in Catchments

JWWTP Jericho Wastewater Treatment Plant

km/yr Kilometer per Year

km2 Kilometers Squared

L/sec Liter per Second

LOM Labile Organic Matter

m Meter

a.m.s.l. Above Mean Sea Level

b.m.s.l Below Mean Sea Level

m3 Cubic Meters

m3/day Cubic Meters per Day

MCM/y Million Cubic Meter per Year

mg/L Milligram per Liter

mm Millimeter

mm/a Millimeter per Annual

mm/yr Millimeter per Year

MAGIC Model for Acidification of Groundwater in

Catchments

MERLIN Model of Ecosystem Retention and Loss of

XII

Inorganic Nitrogen

MISO Multi Input Single Output

mS Millisiemens

NBOD Nitrogenous Biochemical Oxygen Demand

NOAA National Oceanic and Atmospheric Administration

PCBS Palestinian Central Bureau of Statistics

PMD Palestinian Meteorological Department

PWA Palestinian Water Authority

ROM Recalcitrant Organic Matter

RWQM Receiving Water Quality Model

S1 First Scenario (current)

S2 Second Scenario (future)

S3 Third Scenario (future)

Sm Mass flux per unit volume.

SMHI Swedish Meteorological and Hydrological Institute

SWAT Soil and Water Assessment Tool

SWMM Storm Water Management Model

TDS Total Dissolved Solids

TKN Total Kjeldhahl Nitrogen

TMDL Total Maximum Daily Load

TN Total Nitrogen

TP Total Phosphorus

TSS Total Suspended Solids

USEPA United States Environmental Protection Agency

USDA United States Department of Agriculture

V Volume of the control volume

VBA Visual Basic for Applications

WWTP Waste Water Treatment Plant

xi Principal directions of the dispersion coefficient

tensor.

XIII

Water Quality Modeling of Al-Qilt Stream

By

Hani Adel Shraideh

Supervisor

Dr. Abdel Fattah Hasan

Co-Supervisor

Dr. Sameer Shadeed

Abstract

Surface water resources are very limited in Palestine, so special interest

must be given to the quantity and quality of such valuable resources. Al-

Qilt streamwater is considered as essential source for agricultural uses. The

water quality of Al-Qilt stream is subjected to several pollutions that

severely affect and limit the full utilization of such valuable source. This

thesis focused on water quality modeling of Al-Qilt streamwater

considering the dissolved oxygen as a key quality parameter. The potential

pollution sources in the area were explored. Using GIS shapefiles, and

Google earth maps, several detailed maps, and elevation profile was

created to describe the properties of the catchment with focusing on the

main stream. Samples were collected regularly from the five selected

locations on a monthly periodic time intervals. For the five locations, on

site tests for the following parameters (DO, pH, Temp, TDS and EC) had

been conducted. Laboratory analyses for (BOD5, BOD20, COD, Total

Nitrogen (TN), Ammonium, Nitrate, Nitrite, and Total Suspended solids

(TSS)) were performed for all the samples in PWA’s laboratory. A water

quality model (QUAL2Kw) was used and three different scenarios were

assessed and simulated to predict the dissolved oxygen concentration levels

XIV

along the stream. Four key input parameters controlled the modeling

process; these are Reaeration, Deoxygenation, Nitrification, and

Denitrification. The first scenario of the model simulated the current

situation of Al-Qilt streamwater, the second scenario simulated the addition

of stepped weirs at certain locations to improve the reaeration process, and

the third scenario simulated the construction of a wastewater treatment

plant to treat the raw wastewater flowing from Qalandia and Al-Ram

region. The results of the reaeration, deoxygenation, and nitrification rates

were much higher than the typical range. Results from the three model

scenarios confirmed that the stream capable to conduct significant self

remediation process that raised the dissolved oxygen concentrations up to

the saturation levels. The proposed reaeration stepped weirs was found as

suitable solution to improve the quality of water upstream and raised the

dissolved oxygen concentrations from 2.5 mg/L up to around 7.5 mg/L.

The effects of the WWTP for the flow running from Qalandia region were

limited on the DO levels with only 4.7% raise.

1

Chapter One

Introduction

1.1 General Background

Water shortage in the West Bank and Gaza Strip is a dominant feature.

Several factors had aggravated the problem of water shortage, such as

climate change, pollution, lack of integrated management strategies and the

unfair Israeli control over Palestinian water resources. Unless Palestinians

gain their access to the Jordan River, they mainly depend on groundwater

to fulfil their domestic, agricultural, and industrial needs. In general

groundwater quality in the West Bank is considered acceptable.

Nevertheless, several sources of pollution are affecting the groundwater

quality in the West Bank. Three possible major pollution sources:

anthropogenic effect, agricultural return flow and deep brine water and

dissolution of salts from Lisan layers (Aliewi et al., 2001).

In this research, the integrated models for water streams models is being

considered as effective tools to simulate the remediation process of

polluted streams. Such tools were applied for Al-Qilt stream, one of the

Jordan River attributes. Surface runoff during winter storms in addition to

treated wastewater effluent from Al-Bireh Wastewater Treatment Plant

(WWTP) contribute to the stream flow of Al-Qilt catchment. Pollution in

Al-Qilt catchment can be attributed to verity of sources that includes

physical, chemical, and biological substances. Human activities are the

main source of pollution. Such activities include continuous discharge of

2

untreated domestic and industrial wastewater, return flow from

uncontrolled agricultural areas and traffic wastes and industrial air

pollutions.

To change the management practices over the catchment, more information

about pollution sources and their impacts on the water quality is required.

However, the available information of water quality at the Palestinian

Waster Authority is limited for the catchment. Therefore, the model in this

study was created with the uttermost available information.

1.2 Problem Statement

Al-Qilt streamwater is considered as an important source for domestic uses,

downstream, for the people living in Aqbet Jaber refugee camp.

Accordingly, any pollutants get into the stream will deteriorate its quality

and therefore will jeopardize the public health in the catchment.

Al-Qilt catchment contains several pollutions sources (point and diffuse

sources) which are distributed randomly over the catchment, such as Israeli

settlements, Israeli military base, uncontrolled agricultural practices, and

the effluent of untreated wastewater. These sources are negatively affecting

the stream’s water quality. Direct effects as in the case of forthright

pollutions, such as raw wastewater flowing into the stream. Indirect effects,

as in the case of Israeli restrictions on the Palestinian management actions.

1.3 Research Motivations

Several reasons urge to study of Al-Qilt streamwater quality; among which

are:

3

1. The catchment is the host of more than 128,000 citizens that are

affected directly and indirectly by the deteriorating water quality

(PCBS, 2007).

2. The water supply for Aqbet Jaber refugee camp downstream depends

partially on the Al-Qilt streamwater. The water supplied by the

stream with unacceptable quality, affecting the public health in the

camp. The quality of the water supplied from the stream to the camp

is highly questionable. Due to the primitive treatment process which

based only on old sand filters.

3. The increasing trend of Palestinians to use the treated water and

wastewater to bridge the increasing supply-demand gap in the West

Bank.

1.4 Research Objectives

The following are the key objectives:

1. To simulate Dissolved Oxygen (DO) in the main stream of Al-Qilt as

a key water quality parameter under current and future conditions.

2. To propose proper remediation options of the local environment

along Al-Qilt stream.

1.5 Research Outputs

The following are the ultimate research outputs:

1. DO model for Al-Qilt streamwater that simulated three different

scenarios. The first scenario represented the current existing

situation, while the other two scenarios represented future suggested

solutions.

4

2. A decision of the proper treatment technique that could be used to

enhance the remediation process. The technical and economical

aspects in addition to the molding results, were important part to

reach the recommended solution.

1.6 Thesis Outline

The thesis is organized in seven chapters. Chapter 1 gives an introduction

along with background information, research problem, motivations,

objectives and the expected outputs. Chapter 2 presents the related

literature review. Chapter 3 presents the research study area. Chapter 4

illustrates the applied methodology and presents laboratory tests and

modeling approach and development of QUAL2Kw models for the case

study. Chapter 5 presents the results and the discussion of the models

results and the characteristics of the stream. Chapter 6 presents the

proposed key conclusions and recommendations.

5

Chapter Two

Literature Review

2.1 Al-Qilt Streamflow Quality and Pollution

The effects of urbanization on the natural resources is the main issue in

most of the environmental studies discussing Al-Qilt catchment. Elevated

concentrations of pollutants in Al-Qilt stream are a concern to rural

communities especially Aqbet Jaber Camp. Since nitrate and dissolved

organics in excess amounts can cause environmental and health problems.

Rural areas, where livestock and drinking water supplies are found in

common locations, are particularly at risk as animal manure contains high

levels of nitrogen and organics. Moreover, many adjacent communities

discharge wastewater freely, in a way that caused the higher risk to pollute

Al-Qilt streamflow (Abu Hilou, 2008).

Due to the absence of efficient treatment plants and control of wastewater

in the West Bank and some Israeli settlements along the Wadis path, this

sewage flows into the natural streams surrounding the basin, which drained

directly into the Wadis runoff, and percolating to the groundwater (ARIJ,

1997). Such pollutants sources comes from west of Ramallah toward Al-

Qilt catchment which influences ground and springs water and make it

deteriorated and unsuitable for different uses and applications. In turn this

pollution, can influence the economic, social and political situation in the

study area. Additional pollutions of the springs due to other sources has

occurred, e.g., Bedouins living at the downstream dumping their

6

wastewater into the stream, leaching from stone quarries and the municipal

and other industrial wastewater that discharging from the eastern side of the

city of Al-Bireh polluting surface and groundwater resources across water

path(Abu Hilou, 2008).

In Al-Qilt catchment, sediments and topsoil are enriched clearly by

anthropogenic pollutants due to discharge of raw wastewater, dumping

sites, roadside and urban runoff, and sometimes due to natural effects.

Since Al-Qilt streamflow is considered one of the important streamflows in

the area and it is used for domestic purposes, there are no guaranties from

pollution if there is no management plan to control the pollution (Samhan,

2013).

In Al-Qilt catchment, there were few studies, for example, CH2MHill,

1999 was one of these. They did a survey and monitored the Eastern basin

of the West Bank. The main objective for their survey was to understand

the wastewater potential and expected pollution to local resources in the

Eastern basin; they monitored and analyzed the following parameters:

Ammonia, Potassium, Nitrates, Chloride and TDS, Antimony, Lead,

Selenium, Thallium, Iron, Beryllium, Mercury, Cadmium and Arsenic.

Results revealed that there were incremental of pollutants levels in the

springs downstream which used for domestic purposes (CH2MHill, 1999).

2.2 Fate and Transport

Sources of pollution are recognized by two types which are both subjected

to fate and transport processes, these sources can be categorised as:

7

Point sources: these have identified location of discharges into streams

such as outfall of sewer pipes.

Examples of point sources include:

1- Discharges from wastewater treatment plants.

2- Operational wastes from industries.

3- Combined sewer outfalls.

Diffuse sources: these have several sources spread over rural or urban

areas, and pollutants passed through several terrains before it reach the

stream, such as surface runoff reaching a stream (Queensland, 2012).

1- Sediments from construction, forestry operations and agricultural

lands.

2- Oil, grease, antifreeze, and metals washed from roads, parking lots

and driveways.

3- Nutrients and pesticides from agricultural areas.

Fate and transport refers to the way chemicals move through the

environment and their ultimate destinations and how they arrive. Defining

the fate and transport for any single contaminant is often complex. Fate and

transport begins with a source point or diffuse source. A chemical's initial

release into the environment and environmental conditions are important

for determining its free moving lifespan and ultimate destination (Samuel,

2013).

Diffusion and dispersion are the processes by which a tracer spreads within

a fluid. Diffusion is the random advection of tracer molecules on scales

smaller than some defined length scale. At small (microscopic) length

8

scales, tracers diffuse primarily through Brownian motion of the tracer

molecules, whereas at larger scales, tracers are diffused by random

macroscopic variations in the fluid velocity. In cases where the random

macroscopic variations in velocity are caused by turbulence, the diffusion

process is called turbulent diffusion. Where spatial variations in the

macroscopic velocity are responsible for the mixing of a tracer, the process

is called dispersion (Chin, 2013).

Al-Qilt catchment includes various activities and land uses such as

agricultural, industrial, urban, and tourism uses. This requires a simulation

model that can integrate several units and incorporate with the complexity

of tenths of parameters and require large measurements databases for

calibration (Gabriele et al., 2009). In such cases, according to Gabriele the

use of the limited available information approach can be critical in order to

provide useful and reliable results.

The effect of each pollution source varies with its nature and with its effect

on the catchment, such as: urban and agricultural runoff affects negatively

the environment and public health, for example:

1. Affecting the streamwater quality and polluting the groundwater

aquifers.

2. Threatening the public health, biodiversity, and aquatic life.

3. Affecting soil fertility which in turns limiting land use for

agricultural activities.

9

4. Hosting several pollutant like viruses, organic and non-organic

matter, chemicals, heavy metals, solids, grease and oil, and many

other profanations in the wastewater.

2.2.1 Dissolved Oxygen Sag Curve

“When a wastewater with significant amount of organic matter is

discharged into a stream or river, the dissolved oxygen level decreases and

drops to a minimum value. As reaeration slowly replenishes the dissolved

oxygen over time and with distance, the stream DO level comes back to

predischarged concentration” (Riffat, 2013). This is known as the DO sag

curve.

The curve is created when the concentration of DO in a stream where

sewage or other pollutant has been discharged is plotted against the

distance downstream from the sewage outlet. Samples of water must be

taken at areas upstream and downstream from the sewage outlet. The

presence of sewage reduces the oxygen content of the water and increases

the Biochemical Oxygen Demand (BOD). This is due to the action of

saprotrophic organisms that decompose the organic matter in the sewage

and in the process use up the available oxygen (Oxygen sag curve, 2004) a

sag curve is shown in Figure 2.1.

10

Figure (2.1): Dissolved Oxygen Sag Curve (Davis and Cornwell, 2008)

The variability of dissolved oxygen concentration in streams is influenced

by many factors in which those major influences can be categorized as

being either sources or sinks. As major sources of dissolved oxygen, the

oxygen are usually obtained from the reaeration/enhanced aeration process,

photosynthesis oxygen production, and introduction of dissolved oxygen

from other sources such as tributaries (Yudianto and Yuebo, 2008). On the

other hands, the depletion of dissolved oxygen can be caused by the

oxidation of organic material and other reduced matter in the water column,

degassing of oxygen in supersaturated water, respiration by aquatic plants,

addition of biochemical oxygen demand by local runoff, removal of oxygen

by nitrifying bacteria, and the oxygen demand exerted by stream bed

sediments. In water quality modeling, most of those processes are

expressed in mathematical terminology in the form of differential

equations. It would be very complex to simulate all of the chemical

11

reactions and biological processes affecting each element. It is also not

necessary or not possible to measure all data from the field site. Therefore,

many available dissolved oxygen models usually use Streeter and Phelps

equations to describe the biochemical oxygen demand and dissolved

oxygen profiles. The simplest form of this equation is usually applied for a

stream characterized by plug flow system with constant hydrology and

geometry under steady state condition (Yuduanti et al., 2008), a typical

pollutant diffusion behavior is shown in Figure 2.2.

The principal equation of the advection-dispersion is (Chin, 2013):

∑

∑

Where:

D: Dispersion coefficient. Sm: is the mass flux per unit volume.

xi: is the principal directions of the dispersion coefficient tensor.

V: is the volume of the control volume. c: is the tracer concentration.

Figure (2.2): Turbulent diffusion of tracer particles in uniform flow (NOAA, 2005)

The solution of the above equation is in Streeter-Phelps model, the model

describes how DO and Chemical Oxygen Demand (COD) degradation will

2.1

http://en.wikipedia.org/wiki/Biochemical_oxygen_demand

12

be in the stream. The equation was derived by Streeter and Phelps in 1925,

based on field data from the Ohio River. The equation is also known as the

DO sag equation (Streeter-Phelps equation, 2013).

Assumptions of Streeter-Phelps Model:

1- Stream is an ideal plug flow reactor

2- Steady-state flow and BOD and DO reaction conditions

3- The only reactions of interest are BOD exertion and transfer of

oxygen from air to water across air-water interface

The Streeter-Phelps equation, assuming a perfectly mixed stream at steady

state is (Chin, 2013):

( )

( )

Where:

D: saturation deficit.

k1: deoxygenation rate constant.

k2: streamreaeration rate constant.

Lca: ultimate CBOD.

Da: initial oxygen deficit.

t: elapsed time.

kn: nitrogenous decay constant.

Lan: ultimate nitrogenous demand NBOD.

2.3 QUAL2Kw Parameters and Theory

QUAL2kw is a one-dimensional water quality model that uses Microsoft

Excel as its data entry, data analysis, and graphical user interface and

Microsoft Excel VBA and FORTRAN 95 as its program languages

(Pelletier et al, 2006).

QUAL2Kw is a framework for the simulation of water quality in streams

and rivers. Dynamic daily heat budget and water quality kinetics are

2.2

http://en.wikipedia.org/wiki/Ohio_River

http://en.wikipedia.org/wiki/Deoxygenation

13

calculated for one-dimensional steady-flow systems. The framework

includes a genetic algorithm to facilitate the calibration of the model in

application to particular water bodies. The genetic algorithm is used to find

the combination of kinetic rate parameters and constants that results in a

best fit for a model application compared with observed data. The

QUAL2Kw framework allows up to three steady-flow synoptic survey data

sets to be simultaneously calibrated to the same set of kinetic rate

parameters and constants (Pelletier, 2005).

“The QUAL2Kw framework includes the following new elements:

pH, alkalinity and total inorganic carbon are simulated. The river’s

pH is then simulated based on these two parameters.

Software Environment and Interface, Q2K is implemented within the

Microsoft Windows environment. It is programmed in the Windows

macro language: Visual Basic for Applications (VBA). Excel is used

as the graphical user interface.

Carbonaceous BOD speciation, Q2K uses two forms of carbonaceous

BOD to represent organic carbon. These forms are a slowly oxidizing

form (slow CBOD) and a rapidly oxidizing form (fast CBOD).

Anoxia, Q2K accommodates anoxia by reducing oxidation reactions

to zero at low oxygen levels. In addition, denitrification is modeled as

a first-order reaction that becomes pronounced at low oxygen

concentrations.

Sediment-water interactions, sediment-water fluxes of dissolved

oxygen and nutrients are simulated internally rather than being

14

prescribed. That is, oxygen (sediment oxygen demand) and nutrient

fluxes are simulated as a function of settling particulate organic

matter, reactions within the sediments, and the concentrations of

soluble forms in the overlying waters.

Hyporheic metabolism, hyporheic exchange and sediment pore water

quality are simulated, including optional simulation of the

metabolism of heterotrophic bacteria in the hyporheic zone.

Automatic calibration, a genetic algorithm is included to determine

the optimum values for the kinetic rate parameters to maximize the

goodness of fit of the model compared with measured data”,

(Pelletier, 2005).

2.3.1 Segmentation and Hydraulics

The model simulate the main stem of a river. Tributaries are not modeled

explicitly, but can be represented as point sources, a scheme for the

segmentation of QUAL2Kw principle is shown in Figure 2.3.

Figure (2.3): QUAL2Kw segmentation scheme (QUAL2KwTheory and Documentation, 2008)

1

2

3

4

5

6

8

7

Non-point

abstraction

Non-point

source

Point source

Point source

Point abstraction

Point abstraction

Headwater boundary

Downstream boundary

Point source

15

2.3.2 Quantities

Temperature.

Conductivity.

Inorganic suspended solids.

Dissolved oxygen.

Slowly reacting CBOD.

Fast reacting CBOD.

Organic nitrogen.

Ammonia nitrogen.

Nitrate nitrogen.

Organic phosphorus.

Inorganic phosphorus.

Phytoplankton.

Detritus.

Pathogen.

Alkalinity.

Total inorganic carbon.

Bottom algae (periphyton) biomass.

Bottom algae (periphyton) nitrogen.

Bottom algae (periphyton) phosphorus.

2.3.3 Inputs

Location, date, numerical integration control options.

Conditions and concentrations of the headwater boundary flow and

the tributary point sources and diffuse sources.

16

Reach segment lengths, elevations, hydraulic geometry (rating curve

or Manning equation inputs for depth and velocity).

Air temperature, dew point temperature, wind speed, cloud cover,

shade.

Light attenuation parameters.

Options for models of solar radiation, evaporation, and long wave

radiation.

Parameters for water quality kinetics rates and constants.

Parameters to control the genetic algorithm for optional automatic

calibration of water quality kinetics rates and constants.

2.3.4 Outputs

Longitudinal predictions of daily minimum, average, and maximum

concentrations for state variables.

Daily predictions of state variables in the water column and

hyporheic pore water.

2.3.5 Key Input Parameters

Four key input parameters used in the modeling process; these are

Reaeration, Deoxygenation, Nitrification, and Denitrification.

Reaeration: is the process by which oxygen is introduced into a water

surface from the atmosphere. In QUAL2kw, the reaeration rate can either

be specified by the user or calculated internally by QUAL2kw using a

variety of prescribed methods. Reaeration rate is described by Owens-

Gibbs, using the following empirical equation

2.3

17

Where H is the average stream depth, and is the average stream velocity,

this formula is valid when the depth range is 0.1 m< H<3 m and the

velocity rage is 0.03 m/s<V<1.50 m/s, this formula applied for the shallow

streams. However, the previous formula is calculated for default

temperature which 20 Co, for realistic representation for the stream

conditions a temperature correction is needed. The following correction

was used:

( )

Where t is the field temperature at each location. The temperature

correction coefficient is commonly taken to be in the range 1.024 to 1.025

(Chin, 2013).

Deoxygenation: is a process in which carbonaceous BOD is biochemically

oxidized to reduced inorganic compounds. The BOD decay rate

traditionally determined in a laboratory might not necessarily be the same

as estimated for a natural stream (Bansal, 1975). It difference from the

BOD rate constant because there are physical and biological differences

between a river and a BOD bottle, this difference recouped by the

following modifications:

Where is the average speed of stream flow, H is the average stream

depth, and is bed-activity coefficient (from 0.1 to 0.6 or more), the

is rate constant determined in laboratory at 20Co. However, the previous

2.4

2.5

2.6

18

formula is calculated for default temperature which 20 Co, for realistic

representation for the stream conditions a temperature correction is needed.

The following correction was used:

( )


correction coefficient is commonly taken to as 1.135 (Chin, 2013).

Nitrification: is a process in which ammonia is transformed to NO-3

nitrogen. The nitrification process is a result of the action of the nitrosomas

and nitrobacter bacteria. Stoichiometrically, the oxygen requirement for the

overall nitrification reaction is 4.56 mg of O2 per milligram of NH+

4 (Chin,

2013). However, since the reaction is autotrophic, oxygen is also produced

as a result of bacterial growth, and the overall oxygen requirement for

nitrification is less than the stoichiometric value.

Nitrification rate was calculated using a plot for the ((

)

) Vs. Distance

(Hasan et al., 2010), the rate K10 was calculating as the following:

( )

After that the default nitrification rate calculated for temperature of 20 Co

using the following:

For realistic representation for the stream conditions a temperature

correction is needed. The following correction was used:

( )

2.7

2.9

2.8

19


correction coefficient is commonly taken to as 1.1(Chin, 2013).

Denitrification: Under anoxic conditions the nitrate-nitrogen ion

becomes the electron acceptor in the organic matter oxidation reaction

(Schindler, 1985). This process represents a loss of nitrogen from the water

since the nitrogen gas produced volatilizes into the air (Chin, 2013).

In this case study no anoxic conditions were exited, so this parameter was

excluded from the model calculations.

2.4 Water Quality Models (Catchment Scale)

Large variety of catchment scale models for water quality modeling was

developed mostly in the US. The variety probably caused by the different

environmental conditions and purposes when the models were developed.

Changes and modification must be taken when using a model in Palestine

to satisfy and meet the model theories. Several models are described below:

1- AGNPS (Agricultural Non-Point Source) pollution model: is a joint

United States Department of Agriculture (USDA) - Agricultural

Research Service (ARS) and - Natural Resources Conservation

Service system of computer models developed to predict non point

source pollutant loadings within agricultural watersheds. It contains a

continuous simulation surface runoff model designed to assist with

determining Best Management Practices (BMPs), the setting of Total

Maximum Daily Loads (TMDLs), and for risk & cost/benefit

analyses, it was developed in 1993 (Bragadin et al., 1993).

20

2- The Swedish Meteorological and Hydrological Institute (SMHI) is a

government agency under the Swedish Ministry of the Environment.

SMHI's mission is to manage and develop information on weather,

water and climate that provides knowledge and advanced decision-

making data for public services, the private sector and the general

public. SMHI aims to contribute to increased social benefit, safety

and a sustainable society. SMHI uses models to study the influence

of climate and nutrient loads on the coastal and marine environment,

in projects including AMBER, Baltic Way, ECOSUPPORT and

INFLOW within the BONUS program, the model was developed in

2001 (Andersson and Arheimer, 2001).

3- The INCA project is based on the INCA (Integrated Nitrogen in

Catchments) model, a processed based representation of plant/soil

system and in stream nitrogen dynamics. The INCA project aims to

use the model to assess the nitrogen dynamics in key European

ecosystems, the model was developed in 2002 (Wade et al., 2002).

4- MAGIC (Model for Acidification of Groundwater in Catchments) is

a process-oriented intermediate-complexity dynamic model by which

long-term trends in soil and water acidification can be reconstructed

and predicted at the catchment scale. MAGIC produces long-term

reconstructions and predictions of soil and stream water chemistry in

response to scenarios of acid deposition and land use. MAGIC uses a

lumped approach in two ways, MAGIC was developed in 1985

(Cosby et al., 1985):

21

1- A myriad of chemical and biological processes active in

catchments are aggregated into a few readily described

processes.

2- The spatial heterogeneity of soil properties within the

catchment is lumped into one set of soil parameters.

5- The Soil and Water Assessment Tool (SWAT) is a public domain

model jointly developed by USDA Agricultural Research Service

(USDA-ARS) and Texas A&M AgriLife Research. SWAT is a small

watershed to river basin-scale model to simulate the quality and

quantity of surface and ground water and predict the environmental

impact of land use, land management practices, and climate change.

SWAT is widely used in assessing soil erosion prevention and

control, non-point source pollution control and regional management

in watersheds, the model was developed in 1993 (Arnold et al.,

1993).

6- MERLIN (Model of Ecosystem Retention and Loss of Inorganic

Nitrogen) is a catchment scale model of linked Carbon and Nitrogen

cycling in ecosystems. The model is split in to two plant

compartments, namely active (plant) and structural (wood) biomass,

and two soil organic compartments termed Labile (LOM) and

Recalcitrant Organic Matter (ROM). Fluxes in and out of the

ecosystem as well as between compartments are regulated by

processes such as atmospheric deposition, hydrological discharge,

plant uptake, litterfall, wood production, microbial N-

http://www.ars.usda.gov/

http://www.ars.usda.gov/

http://agriliferesearch.tamu.edu/

http://www.macaulay.ac.uk/dynamo/glos.htm

22

immobilisation, mineralisation, nitrification, and denitrification. The

rates of fluxes are controlled by the C/N ratios of organic

compartments as well as the inorganic N concentrations in the soil

solutions; the model was developed in 1997 (Emmett et al., 1997).

2.5 Case Studies

Several studies and researches have been done to study the field of water

quality modeling. Models have been made for rivers, lakes, and

catchments. A summary for specific studies related to these topics are

presented below:

(Alawneh, 2013) studied the Modeling of Water Quality and Quantity for

Faria Streamusing QUAL2Kw to create the model, and to do an assessment

of Faria stream quality variations of TKN, TDS, TSS, EC, pH using

Microsoft excel, and to create a DO profile for the stream by QUAL2Kw

model for summer with high BOD levels and for some critical conditions

of minimum DO level with minimum flow. Modeling results showed there

is a good correlation of simulated flow, depth, velocity, travel time, DO

profile.

(Will et al., 2012) studied the Catchment-Scale Hydrologic and Water

Quality Modeling using the Storm Water Management Model (SWMM) to

validate lake Tahoe TMDL in South lake Tahoe. Their objective was

developing average annual pollutant load estimates for urban catchments,

and to simulate summer storm event response. The used tool was Pollutant

Load Reduction Model (PLRM), and SWMM. The study utilized a

comparison of the modeled storm event results to measured flow and fine



23

sediment particle concentrations allows for evaluation of model

performance and parameter refinement.

(Subhi et al., 2012) studied the Anthropogenic trace metals and their

enrichment factors in Wadi Al-Qilt sediment, Palestine. The main objective

was to delineate the extent of heavy metal pollution from Al-Qilt sediment.

The Enrichment Factor (EF) values were determined for heavy and trace

metals for the tested sediment samples. The surface sediment samples of

the Wadi Al-Qilt catchment were characterized by trace metals that are

typical of aquatic environments located in industrial and densely populated

areas.

(Iqbal et al., 2010) studied the Development of a Catchment Water Quality

Model for Continuous Simulations of Pollutants Build-up and Wash-off in

Gold Coast, Australia. Their objective was to estimate of runoff water

quality parameters conducted to determine and appropriate water quality

management options and practices. They used Runoff Model and Pollutant

Model. The developed runoff water quality model was set-up to simulate

the build-up and wash-off of Total Suspended Solids (TSS), Total

Phosphorus (TP) and Total Nitrogen (TN).

Rebecca. (2010) studied the water quality modeling for the Kennet and

Avon Canal, a navigational canal in an inland catchment in Kennet and

Avon Canal in southern England. Her objective was to evaluate of six

management scenarios proposed by the Environment Agency to address the

water quality problem using Algorithm Model. This project identified the

key solids generation and transport processes to be included in a water

24

quality model for inland navigational canals. The model suggested that

filtration or other treatment of water in the canal near the confluence with

the river is the best management option.

(Saed., 2009) studied the hydrochemical variation in the springs water

between Jerusalem–Ramallah Mountains and Jericho Fault, Palestine. His

objective was to increase the efficiency of freshwater exploitation in the

region. Some precautions, however, should be taken in future plans of

artificial recharge of the aquifers or surface-water harvesting in the Wadi.

Two zones of recharge are distinguishable. The first zone represented by

Fara spring and Al-Qilt spring which was fed directly through the

infiltration of meteoric water and surface runoff from the mountains along

the eastern mountain slopes with little groundwater residence time and high

flow rate. The second zone was near the western border of Jericho at the

foothills, which is mainly fed by the under-ground water flow from the

eastern slopes with low surface infiltration rate.

(Mazdak et al., 2006) studied the role of watershed subdivision on

modeling the effectiveness of BMPs in Raccoon, Iowa, USA. Using the

Soil and Water Assessment Tool (SWAT). Their objective was to assess

the ability of the SWAT to simulate stream flow and associated movement

of nitrogen, phosphorus, and sediment. Results for the study watersheds

indicated that evaluation of the impacts of these BMPs on sediment and

nutrient yields were very sensitive to the level of subdivision that was

implemented in the modeling tool SWAT.

http://en.wikipedia.org/wiki/Iowa

25

(Romanowicz et al., 2004) studied the Water Quality Modeling in Rivers

with Limited Observational Data in River Elbe, Germany. Their objective

was to find a derivation of a data-based model that has the minimum

number of parameters. Using Data-Based Mechanistic Model (DBM).The

result of this analysis was a nonlinear, Multi Input Single Output (MISO)

transfer function model that provides a statistical counterpart of the

mechanistic algae model.

(Simon and Mohand., 2003) studied the modelling scenarios for south east

queensland regional water quality management strategy in Wye catchment,

England. Their objective was to examine the spatial distribution of nutrient

pollution risk and to assess broad-scale spatial and temporal variability in

nutrient fluxes, using a Receiving Water Quality Model (RWQM2).The

model was calibrated/verified, and after the development of realistic

scenarios within the limitations of the model, the RWQM2 was then used

to produce results for the defined management scenarios for dry average

and wet years.

26

Chapter Three

Study Area

3.1 Geography and Topography

Al-Qilt catchment is located in the western side of the Jordan Valley in the

West Bank, Palestine with a total area of about 174 km2

.The catchment

extents over parts of Ramallah, Al-Bireh, Jerusalem and Jericho as shown

in Figure 3.1. The main stream is 38 km long which starts from Al-Bireh

city with upstream elevation of 727 m a.m.s.l, and ending at the vicinity of

Jordan River with downstream elevation of 178 m b.m.s.l., passing through

Burqa, Mukhmas, Aqbet Jaber Camp, and Jericho (see Figure 3.1). Al-Qilt

catchment located in the well-known as Dead Sea Rift Valley. The

elevation of the Rift Valley drops to about 350 m b.m.s.l. to the present

shores of the Dead Sea in the east, and the west of the Rift Valley in the

vicinity of Ramallah and Jerusalem the mountains rise up to elevations over

800 m a.m.s.l. which creates a steep and sharp slopes (ARIJ, 1995).

The catchment includes five major springs, which are: (Ein Jumeiz, Ein

Fara, Fawwar, Ras Al-Qilt, and Ru’yan. Al-Qilt catchment is bounded by

Nueima drainage basin from north, Soreq and Al Dilb drainage basins from

west, Mukallak and Marar drainage basins from south and Jordan River

from the east (Ghassan, 2009).

Al-Qilt catchment contains two main tributaries. The first tributary is called

Wadi Sweanit which originates from the eastern part of Al-Bireh. Wadi

Sweanit contains two water springs, which are Fawwar and Ras Al-Qilt.

The second tributary named as Wadi Fara and contains three water springs,

27

which are Ein Jumeiz, Ein Fara and Ru’yan (Subhi et al., 2012). This study

focuses on Wadi Sweanit due to its continuous streamflow over the year,

unlike Wadi Fara which is only seasonal.

Figure (3.1): General map of Al-Qilt catchment location

3.2 Stream Description

Several field visits were conducted to assess the physical, chemical, and

biological characteristics of Al-Qilt stream. Results are listed in Table 3.1,

a list of these characteristics with some details; further descriptions are

presented in the following chapter 4.

Al-Qilt Catchment

28

Table (3.1): Factors used in stream survey and assessment Physical Measures Chemical Measures Biological Measures

Size (width, depth) Dissolved oxygen

Fish Flowrates, velocity Nitrogen

Reaeration rates Suspended solids

Slope Phosphorus

Phytoplankton Pool, and riffles pH

Temperature Dissolved solids

Sedimentation

The average and maximum slope of the stream was 5.3% and max. 20.3%,

respectively. The pools and riffles phenomenon was limited and the stream

is best described with Owens-Gibbs empirical formulas according to the

streamflow velocity, width and depth. From observation during site visits,

no considerable sedimentations had accumulated along the stream’s bed

due to the high flow velocity and high slope.

The stream especially downstream was full of aquatic life with fishes and

frogs, with almost no phosphorus nor phytoplankton.

3.3 Population

Many Palestinian communities and Israeli settlements are located within

the catchment boundary which affects the environment and the streamwater

quality. According to (PCBS. 2007), the Palestinian population in the

catchment was estimated to be more than 128,049 inhabitants (see Table

3.2). However, the number of Israeli settlers in six settlements was

estimated to be more than 29,250 settlers (see Table 3.3). Palestinian and

Israeli built up areas are about 1.7% and 1.5%, respectively (PCBS. 2007).

Further information is still needed about the Israeli military bases and

29

industrial zones, since very limited and inaccurate information was

available.

3.4 Climate

The climate of the West Bank has no different conditions of the

Mediterranean climate. There are two significant seasons: the summer

which is dry hot season from June to October, and winter which is cold wet

season from November to May. In spite of that West Bank has a small area;

there is a significant difference in the climate. Such variations are clear in

Al-Qilt catchment. In the western part of the catchment, the climate is

influenced by the Mediterranean climate, a rainy winter and dry summer.

Table (3.2): Population of

Palestinian communities

Community Pop.

Beitin 2014

Al-Bireh 35,910

DeirDibwan 4,937

Burqa 1,964

KafrAqab 10,103

Qalandia Camp 7,962

Mukhmas 1,305

Al Ram and Dahyiat

Al Bareed 18,356

Jaba' 2,870

Hizma 5,645

Beit Hanina 966

Anata 10,864

EinAduyuk At Tahta 783

Jericho 17,515

Deir Al-Qilt 4

AqbatJaber Camp 6,851

Total 128,049

Table (3.3): Population of

Israeli settlements

Settlement Pop.

Psagot 1,333

KokhavYa’kov 3,922

Ma’aleMukhmas 998

Almon 740

Giv’a Binyamin

(Adam) 1,988

NevehYa’kov 20,269

Total 29,250

30

While the climate in the eastern part is classified as arid with hot summers

and warm winters, (Ghassan, 2009).

In the western part of Al-Qilt catchment, the average temperature ranges

between 6–12 °C in the coldest month (January) and between 22–27 °C

during the warmest month (August) in the western part of the catchment,

while in the eastern part of the catchment it ranges between 7–19 °C during

(January) and between 22–38 °C during (August) (PMD, 2012). A map for

the mean annual temperature ranges over Al-Qilt catchment is shown in

Figure 3.2.

Figure (3.2): Average annual temperature ranges of Al-Qilt Catchment

31

3.5 Rainfall

Rainfall ranges from 5 to 100 mm in each storm event. Spatial distribution

of rainfall also varies strongly. Average annual rainfall in Ramallah and

Jerusalem mountains ranges from 400 to 650 mm, whereas in Jericho, the

average annual rainfall is about 180 mm, of which approximately 60% falls

in the three months of December, January and February. Figure 3.3 shows

the average annual rainfall of Ramallah station for the period of (1990 –

2007). In general, Jericho district has the lowest rainfall in the region and

short rainy season ranging between 20-25 rainy days per year (ARIJ, 1997;

PWA, 2007). Figure 3.4 shows the rainfall contour map for Al-Qilt

catchment in 2012.

Figure (3.3): Annual rainfall of Ramallah

0 200 400 600 800 1000 1200 1400 1600 1800

1990/1991

1991/1992

1992/1993

1993/1994

1994/1995

1995/1996

1996/1997

1997/1998

1998/1999

1999/2000

2000/2001

2001/2002

2002/2003

2003/2004

2004/2005

2005/2006

2006/2007

Rainfall (mm)

Ye

ars

32

Figure (3.4): Rainfall contour map for Al-Qilt Catchment.

3.6 Streamflow

The long-term observations of the streamflow (runoff) that generating over

Al-Qilt catchment ranged from 3.0 to 10.0 MCM/year. Flow measurements

are taken for (Al-Bireh, Mukhmas, Fawwar, Ras Al-Qilt, and Murashahat).

The total wastewater flow which discharged into Al-Qilt catchment from

the Palestinian and Israeli sides could be estimated about 5 MCM/year

(Samhan, 2013).

The flow measurements of the five sampling points on the main stream

of Al-Qilt catchment are presented in Table 3.4:

Table (3.4): Flow measures for the five sampling locations Community Flow (m

3/d)

Al-Bireh 5,000

Mukhmas 3,374

Fawwar 6,726

Ras Al-Qilt 17,458

Murashahat 15,378

33

These values estimated according the annual averages of streamflow runoff

from 2007 to 2012, and not only for wet or dry seasons. The discharges

from the five springs depend on the rainfall amount for the corresponding

year. From flow measurements it is believed that Al-Qilt spring has the

highest discharge quantity which promotes further concern to protect it.

34

Chapter Four

Methodology

4.1 Introduction

This chapter discusses the scientific approach that was used to build the

DO model for Al-Qilt streamwater. The overall research methodology is

presented in Figure 4.1.

Figure (4.1): Research methodology

35

4.2 Field and Laboratory Work

4.2.1 Site Investigation and Characterization of the Study Area

A field visit for the study area was conducted on the 6th

of March 2013; in

which the following sites were visited:

1- Al-Bireh WWTP.

2- Fawwar, and Ras Al-Qilt springs.

3- Mukhmas village.

4- Aqbat Jaber refugee camp.

The potential pollution sources in the area were explored, such as (Israeli

military zone, solid waste dumping site, and agricultural zones).

A general view was taken for the terrain of the area, topography, and land

cover/use. Using the GIS shapefiles that were obtained from PWA, and

Google earth maps. Several detailed maps were created. Also, an elevation

profile was created showing (longitudes, altitudes, average slopes, and

elevations are shown in Figure 4.2). These maps were used to describe the

properties of the catchment, focusing on Al-Qilt main streamwater.

4.2.2 Allocating Sampling Locations

Several considerations and criteria were taken in allocating the sampling

locations, such as:

1- Drastic changes in slope or flow.

2- Existence of aquatic growth or pollution sources.

3- Ease of accessibility.

4- Historical data of sampling points.

36

Figure (4.2): Elevation profile of Al-Qilt stream

A description about the sampling points is presented in Table 4.1.

Table (4.1): Sampling locations description

Location Description Distance*

(km)

Al-Bireh

WWTP

Samples were taken exactly after the

TP; samples at this location were taken

from the treated wastewater effluent.

This location considered as reference.

0

Mukhmas

Samples were taken exactly after

Mukhmas village; samples at this

location were untreated wastewater

samples, since the stream running from

Al-Bireh merged with raw wastewater

coming from Qalandia region.

5.5

Fawwar

Samples were taken exactly at the

spring outlet; samples at this location

were fresh water.

17

Ras Al-Qilt

Samples were taken exactly at the

spring outlet; samples at this location

were fresh water.

21.7

Murashahat

Samples were taken before the

filtration process; samples at this

location were fresh water samples.

27

Distances are relevant to the discharge at the point of Al-Bireh WWTP to Al-Qilt

stream.

37

The total number of samples that were taken is 15, water and wastewater

(treated and untreated) were taken directly from the stream. Samples were

collected from both upstream and downstream of Al-Qilt streamwater. The

upstream section extends from Al-Bireh WWTP passing through Mukhmas

village; with total length of 10.5 km. The downstream section extends from

Fawwar spring and ends at Aqbet Jaber camp; with total length of 10.5 km

(see Figure 4.3).

Figure (4.3): Sampling locations map

4.2.3 Sampling Frequency

Samples were collected on monthly basis from the five selected locations.

The sampling period covered the dry season of 2013; from April till June of

2013.

38

4.2.4 Samples Collection

Samples were collected (see Figure 4.4) according to the following

procedures:

1- The containers were washed by the stream water at each

corresponding location before using each container.

2- The collected volume of each sample was 1 liter.

3- The samples were collected in High density Poly Ethylene (HDPE)

bottles with tight caps.

4- The samples were collected by a sampler as shown in Figure 4.4.

5- The numbers of samples and dates had been written on each

container.

6- The samples had been cleaned from visible relatively large

suspended objects.

7- The samples were preserved during the tour, in a cooled ice

container.

Figure (4.4): Collecting samples from Al-Qilt stream

39

4.2.5 Samples Analyses and Stream Characterization

4.2.5.1 Field Tests

For each collected sample, the following parameters (DO, pH, Temp, TDS

and EC) were determined on site.

4.2.5.2 Laboratory Analyses

The following analyses were performed for all the samples in PWA’s

laboratory in Ramallah: (BOD5, BOD20, COD, Total Nitrogen (TN),

Ammonium, Nitrate, Nitrite, and Total Suspended solids (TSS)).

4.3 Setting up the Model

Three scenarios were simulated for Al-Qilt streamwater; one scenario for

the current situation, and two scenarios for suggested future situations.

4.3.1 Current Situation Scenario

The first scenario simulated the current situation without any remediation

or reconditioning for the two reaches (upstream and downstream). This

case simulated the first three months of the dry season (April, May, and

June of 2013) no further extension for the study period included since the

stream usually dries up between July and August of every year until the

next wet season which starts at late September to mid of October. The

simulated condition here represented the worst case scenario with

minimum DO level and low quantities of fresh water flow.

4.3.2 Future Model Scenarios

The second scenario simulated the effect of adding two artificial weirs in

the upstream reach on increasing the DO levels. Figure 4.6 shows the

mechanism which was proposed to increase the DO in the stream. The

suggested step elevation of the weirs was, h = 1 ft (Butts and Evans, 1983).

40

Figure (4.5): Stepped aeration cascades

The predictive relation of the aerated oxygen assumes that saturation

concentration (Cs) is constant and determined by the water−atmosphere

partitioning. If that assumption is made, Cs is constant with respect to time,

and the oxygen transfer efficiency (aeration efficiency), E may be defined

by the following equation (Baylar et al., 2009):

Where u and d indicating upstream and downstream locations, respectively.

The efficiency of the aeration enhancement was set to 85% for all the

proposed weirs. The DO concentrations before the proposed weirs were

determined from the current situation scenario results and using the

efficiency equation, the values of the new generated DO concentrations

were calculated and used in building the future model scenarios.

The third scenario, was exactly as the second one, just with the addition of

WWTP to treat the raw wastewater flowing from Qalandia region.

The modeling steps for building future model scenarios did not changed

from the followed steps in building the current situation scenario model.

4.1

41

Chapter Five

Results and Discussion

5.1 Introduction

This chapter discusses the measured and calculated values of BOD, COD,

EC, TS, TSS, TDS, TN, NO-3, NO

-2, Temperature, pH, and constant rates

that were used, in order to set up the QUAL2Kw model. This chapter also,

presents the results of the three model scenarios that were created for Al-

Qilt streamwater.

5.2 Stream Characterization

5.2.1 Physical Characteristics

The physical characteristics of upstream and downstream reaches were

specified. Variations of these characteristics at the five allocated sampling

locations are listed in Table 5.1. The highest measured flow rate along the

stream was at Ras Al-Qilt, this is because of Ras Al-Qilt spring which

feeds the stream at this location. While the lowest measured flow rate along

the stream was at Mukhmas due to the filtration process. The velocity of

the stream ranged between 0.203 m/s at Mukhmas, where the stream is flat

and wide; and 1.37 m/s at Murashahat, where the stream is steep and

narrow.

42

Table (5.1): Summary of physical characteristics of Al-Qilt stream

Location Flow (m3/s) Velocity (m/s) Depth (m) Width (m)

Al-Bireh WWTP 0.0578 0.35 0.18 1.15

Mukhmas 0.039 0.203 0.2 1.2

Fawwar 0.0778 0.324 0.25 1.2

Ras Al-Qilt 0.202 0.315 0.4 2

Murashahat 0.1779 1.37 0.25 0.65

Al-Qilt streamwater gained and loosed different quantities of water over

the study period, in both upstream and downstream reaches (Figure 5.1).

Figure (5.1): Gaining/Losing location sketch along the stream

Losing (0.0188

m3/s)

Seasonal dry

reach

Gaining (0.1242

m3/s)

Losing (0.0241

m3/s)

43

5.2.2 Field Measured Characteristics

DO – On April, 2013; Do values ranged from 11 mg/L at Murashahat to 3

mg/L at Al-Bireh (Figure 5.2). On May, 2013; Do values ranged from 13.7

mg/L at Ras Al-Qilt to 2.5 mg/L at Al-Bireh (Figure 5.3). On June, 2013;

Do values ranged from 12.9 mg/L at Murashahat to 3.6 mg/L at Al-Bireh

(Figure 5.4). The reasons of these variations are the different levels of

pollution, temperatures, and reaeration process. Each measured value of

DO was associated with specific temperature. Temperatures on

downstream locations were lower than downstream location; since samples

on downstream were taken in the early morning; and samples on upstream

were taken in the afternoon.

Figure (5.2): Measured DO concentrations with associated temperatures, on April 2013

3

4.7

7.2

9.8

11

20

21.5

22.2

22.5

20.5

19.5

20

20.5

21

21.5

22

22.5

23

0

2

4

6

8

10

12

Al-Bireh WWTP Mukhmas Fawwar Ras Al-Qilt Murashahat

Tem

pe

ratu

re (

Co)

DO

(m

g/L

)

Location

DO mg/L on April Temperature on April

44

3.6

5.2 6.3

9.4

12.9

26.1

30

22.6

25.7 28

0

5

10

15

20

25

30

35

0

2

4

6

8

10

12

14

Al-BirehWWTP

Mukhmas Fawwar Ras Al-Qilt Murashahat

Tem

per

atu

re (

Co)

DO

(m

g/L)

Location

DO mg/L on June Temperature on June

Figure (5.3): Measured DO concentrations with associated temperatures, on May 2013

Figure (5.4): Measured DO concentrations with associated temperatures, on June 2013

DO concentrations varied from one location to another. The effluent from

Al-Bireh WWTP had the lowest DO concentration along the main stream

2.5 2.5

7.1

13.7

11.8

24.3

26.5

22.9

25.8 25.5

22.5

23

23.5

24

24.5

25

25.5

26

26.5

27

0

2

4

6

8

10

12

14

16


Tem

per

atu

re (

Co)

DO

(m

g/L)

Location

DO mg/L on May Temperature on May

45

in both reaches upstream and downstream, with range of (2.5 – 3.6) mg/L.

The concentration increased to range of (2.5 – 5.2) mg/L at Mukhmas

sampling location which is located approximately 5 km from Al-Bireh

WWTP. The DO concentration increased despites of the untreated

wastewater from Qalandia region which mixes with Al-Qilt main

streamwater.

Murashahat sampling location had high DO concentration with range of

(11-12.9) mg/L, and Ras Al-Qilt sampling location had a range of (9.4 –

13.7) mg/L. Oxygen deficits for all the collected water samples are listed in

Table 5.2. Springs discharges had higher DO concentrations along the main

stream.

Table (5.2): Dissolved Oxygen deficit between measured and saturation

concentrations

Location Al-Bireh Mukhmas Fawwar Ras Al-Qilt Murashahat

Samples Collected During April (mg/L)

DO 3.00 4.70 7.20 9.80 11.00

DOs 8.29 8.30 8.49 8.62 7.17

Oxygen

Deficit 5.29

3.60 1.29

0 0

Samples Collected During May (mg/L)

DO 2.50 2.50 7.10 13.70 11.80

DOs 7.67 7.51 8.45 8.07 8.33

Oxygen

Deficit 5.17

5.01 1.35 0 0

Samples Collected During June (mg/L)

DO 3.60 5.20 6.30 9.40 12.90

DOs 7.38 7.99 8.53 8.14 7.98

Oxygen

Deficit 3.78

2.79 2.23 0 0

46

Each sample location had its related DOs according to the corresponding

temperature (Environmental Services Program, 2013).

pH – From April until June, 2013 values of pH were increasing with range

of (1.07 – 1.36). On the other hand, pH vales were almost constant for each

location at the same month. More details for pH values in appendix A

TDS/EC – From April until June, 2013 values of the TDS concentrations

were extremely close with very limited differences. The same observation

for the TDS values applied to the EC values see Table 5.3.

Table (5.3): Measured EC values for samples on April, May, and June

2013


EC

(mg/L)

Samples Collected During April

1345 1375 635 530 534

Samples Collected During May

1360 1388 635 507 497

Samples Collected During June

1350 1355 654 550 472

5.2.3 Laboratory Characteristics

BOD – On April, 2013 values of BOD in the five sampling locations were

roughly three times higher than May and June, 2013 this is due to the

cultural habits for the shepherds in the region of washing their sheep in the

stream on April of each year, and because of the seasonal visits to this

location by the tourists. On the other hand, relatively close match was

noticed for the five sampling locations, between BOD values on May and

June, 2013 (Table 5.4). Only Ras Al-Qilt and Mukhmas had some

differences in the BOD values between May and June, 2013. Values of

BOD20 and a figure for BOD5 are listed in appendix B.

47

Table (5.4): BOD ranges for the five sampling locations

Location

BOD5

Range

(mg/L)

Note

Highest

BOD20

(mg/L)

Note

Al-Bireh 5 - 20

May values

exactly

matched June

values

35

Highest

value on

June, 2013

Mukhmas 15 - 35

No match

between May

values and

June values

55

Highest

value on

April, 2013

Fawwar 5 - 35

May values

almost

matched June

values

35

Highest

value on

April, 2013

Ras Al-Qilt 0 - 15

No match

between May

values and

June values

20

Highest

value on

April, 2013

Murashahat 5 - 15

May values

exactly

matched June

values

15

Highest

value on

April, 2013

BOD20 values for Al-Bireh reached up to 35 mg/L on June, 2013 which is

reasonable since water flowing at this location was a treated wastewater

from Al-Bireh WWTP.

BOD20 values for Mukhmas reached up to 55 mg/L on April, 2013 this

value shows that the flow contains unacceptable concentrations of raw

wastewater and it exceeds the limits of Palestinian standards for treated

wastewater.

BOD20 values for Fawwar reached up to 35 mg/L on April, 2013 where

theoretically it must be zero since the streamwater on this location is fresh

48

water, but it is a strong indicator for an underground pollution source form

Mukhmas region causing this significant BOD level.

BOD20 values for Ras Al-Qilt reached up to 20 mg/L on April, 2013 where


water, but it was due to the tourist’s visits to this location by this time of

the year. The zero value here was due to inaccurate analysis process.

BOD20 values for Murashahat reached up to 15 mg/L on April, 2013 where


water, but due to the sheep cleaning activities, BOD values had increased.

COD – From April, 2013 to June, 2013 COD values ranged from 200 mg/L

to 380 mg/L, these results were in the upstream reach which is considered

as treated and mixed raw wastewater (Figure 5.12). Such values are

considered as moderate, comparing them to the values of COD in

Palestinian wastewater which could reach more than 1000 mg/L in some

cases. Downstream reach had one odd value of COD, at Ras Al-Qilt which

was (323 mg/L) this might be due to the human tourism activities on that

location at this time of the year.

49

Figure (5.5): COD for the five sampling locations on April, May, and June 2013

Nitrogen (TN, TKN, NO-3 and NO

-2)

TN – Values of TN were very reasonable. Comparing the highest value

which was almost 42 mg/L on April, 2013 at Mukhmas (see appendix C)

with the typical value of TN in residential untreated wastewater which is 40

mg/L, showed close proximity. High values appeared at locations which

considered as fresh water on April due to the human activities at this time

of year.

TKN – The maximum value of the TKN was 16.4 mg/L on April, 2013 at

Fawwar (see appendix C), which is significantly lower than the typical

values of TKN in residential untreated wastewater which is 50 mg/L.

NO-3 , NO

-2 - Theoretically NO

-3 and NO

-2 must be zero in the untreated

wastewater. However, because of the considerable aeration that occurred

along the Al-Qilt streamwater, NO-3 and NO

-2 appeared due to nitrification.

261

379

144

323

132

336

212

121 136

112

244

308

100 124

160

0

50

100

150

200

250

300

350

400

Al-BirehWWTP


CO

D (

mg/

L)

Location

COD mg/L on April COD mg/L on May COD mg/L on June

50

Values of NO-3 and NO

-2 were the lowest on June for all the sampling

locations (see Appendix C).

Table 5.5 lists the levels of (total nitrogen, TKN, NO-3, and NO

-2) for the

period from April 2013 to June 2013.

Table (5.5): Nitrogen levels for Al-Qilt stream on April, May and June

2013 Nitrogen levels during April


TN 17.6 41.7 40.2 28.5 38.7

TKN 7.4 11.1 16.4 3.7 11

NO-3 6.6 11.6 20.4 22.1 19.2

NO-2 3.6 19 3.4 2.7 8.5

Nitrogen levels during May

TN 39.3 39.6 34.8 24.9 33.1

TKN 14.1 10.1 2.2 4.3 7.9

NO-3 21.2 18.3 30.6 17.6 15.2

NO-2 4 11.2 2 3 10

Nitrogen levels during June

TN 22.88 34.93 15.57 18.68 28.88

TKN 15.18 14.63 3.07 8.08 14.08

NO-3 5.4 17.9 6.2 9.4 13.3

NO-2 2.3 2.4 1.3 1.2 1.5

TSS – Values of the TSS for the five sampling locations showed

significantly high values on April (Figure 5.17), that exceeded

approximately five times the typical value of TSS in residential untreated

wastewater which is 220 mg/L, especially at the upstream reach, since it

contains treated and mixed raw wastewater.

51

Figure (5.6): TSS for the five sampling locations on April, May, and June 2013

5.3 Modeling Scenarios

5.3.1 Rate Constants

Three key water quality rate constants were calculated (reaeration rate,

deoxygenation rate, and nitrification rate).

Reaeration

Values of the calculated reaeration rates exceeded the typical range of

reaeration which is from 0.1 day-1

for small ponds and backwaters, to 1.15

day-1

for rapids and waterfalls (Tchobanoglous and Schroeder, 1985). The

range of the calculated reaeration rate was (14 – 103) day-1

(Table 5.6). The

maximum value was at Murashahat, since Murashahat has the steepest

section of the stream with significant air mixing. In addition, that

Murashahat had the highest velocity of all locations with 1.37 m/s.

846

1012

561

464 483

289 340

296

372

565

331

260

131 149 144

0

200

400

600

800

1000

1200


TSS

(mg/

L)

Location

TSS mg/L on April TSS mg/L on May TSS mg/L on June

52

Table (5.6): Reaeration rate and temp. correction for Al-Qilt stream on

April, May and June 2013 Reaeration rate during April


kr20 62.832 35.894 32.492 13.364 85.373

krt 62.832 37.194 34.233 14.181 86.392

Reaeration rate during May

kr20 62.832 35.894 32.492 13.364 85.373

krt 69.578 41.877 34.806 15.335 97.269

Reaeration rate during June

kr20 62.832 35.894 32.492 13.364 85.373

krt 72.612 45.501 34.559 15.299 103.210

Deoxygenation

Values of the calculated deoxygenation rates exceeded the typical range of

deoxygenation which is from 0.05 day-1

for untreated wastewater, to 0.7

day-1

for unpolluted river (Thomann and Mueller, 1987), (Kiely, 1997),

(Davis and Masten, 2004) .The range of the calaculated deoxygenation rate

was (0.96 – 9.7) day-1

(Table 5.7). The maximum value was at Murashahat,

since Murashahat has the steepest section of the stream. In addition, that

Murashahat had the highest velocity of all locations with 1.37 m/s. BOD

rate constant (KBOD) parameter controlled the deoxygenation rate. Values of

KBOD are listed in Table 5.8.

53

Table (5.7): Deoxygenation rate and temp. correction for Al-Qilt

stream on April, May and June 2013 Deoxygenationrate during April


kd20 1.396 0.959 1.007 0.702 0.959

kdt 1.396 1.159 1.331 0.964 1.159

Deoxygenationrate during May

kd20 1.396 0.959 1.007 0.702 0.959

kdt 2.407 2.184 1.454 1.464 2.184

Deoxygenationrate during June

kd20 1.396 0.959 1.007 0.702 0.959

kdt 3.023 3.402 1.400 1.445 3.402

Table (5.8): KBOD rate constant and temp.correction for Al-Qilt stream

on April, May and June 2013

BOD rate constant during April


kBOD20 0.230 0.350 0.230 0.230 0.230

kBODt 0.230 0.374 0.254 0.257 0.235

BOD rate constant during May

kBOD20 0.230 0.350 0.23 0.230 0.230

kBODt 0.280 0.471 0.262 0.300 0.296

BOD rate constant during June

kBOD20 0.230 0.350 0.230 0.230 0.230

kBODt 0.304 0.554 0.259 0.298 0.332

CBOD Hydrolysis Rate

The CBOD hydrolysis constant rate was assumed to be equal to 0.1 day-1

,

as an approximate value since the typical range is (0.02 – 10) day-1

(Tech,

2009). Literature values were used as a first approximation and their value

fine tuned through the process of calibration.

54

Nitrification

Values of the calculated of the nitrification rates exceeded the typical range

which is from 0.1 day-1

to 15.8 day-1

(Ruane and Krenkel, 1975), the

calculated range of the nitrification rate was (24.4 – 133.6) day-1

.

Nitrification rates were calculated using a plot for the ((

)

) Vs.

Distance. K10 rate was calculating, using Eq. (2.7), values of K10 were

determined by the division of the curve intersection with the Y axis over

the curve slope. The used figures are presented in appendix D.

Nitrogen Hydrolysis Rate

The organic nitrogen hydrolysis constant rate was assumed to be equal to

0.2 day-1

, as an approximate value since the typical range is (0.001 – 1)

day-1

(Tech, 2009). Literature values were used as a first approximation

and their value fine tuned through the process of calibration.

Denitrification

The constant rate for the denitrification was not used since in this case

study the coditions were aerobic along the whole stream, no anoxic nor

anaearobic conditons existed, so no denitrificationwould occurr.

5.3.2 Current Situation (S1)

This case represented the first three months of the dry season (April, May,

and June of 2013). No further extension for the study period included since

the stream usually dries up between July and August of every year till the

next wet season which starts at late September to mid of October.

DO (Upstream) – Results from the simulation for the upstream showed an

incremental behavior in the DO concentrations over April, May. And June,

55

2013 (Figures 5.18, 5.19, and 5.20). The behavior had limited distortion

between Al-Bireh and Mukhmas locations; this is due to the model trying

to approximate the simulated values to the measured values of DO

concentrations. However the model at this point failed in some how to

match the simulated and measured values. On April, 2013 the value of

simulated DO concentration was 6.93 mg/L which is 47% higher than the

measured value that was 4.7 mg/L. On May, 2013 the value of simulated

DO concentration was 6.9 mg/L which is 76% higher than the measured

value that was 2.5 mg/L. On June, 2013 the value of the simulated DO

concentration was 6.1 mg/L which is 17% higher than the measured value

that was 5.2 mg/L.

Values of the simulated DO concentrations after Mukhmas location showed

significant increase that almost reached the saturation levels. The cause of

the significant increase of the DO concentrations after Mukhmas is the

absence of pollution sources at these locations. The area is rural which gave

the stream the chance to self remediates.

The saturation DO curve showed slight incremental behavior with distance

over the three time steps, almost 0.45 mg/L. This is due to the fact that

water holds more DO in lower altitudes.

56

Figure (5.7): Simulated DO levels for current situation in the upstream reach on April

2013

Figure (5.8): Simulated DO levels for current situation in the upstream reach on May 2013

2.50 2.85

3.33

4.08

4.61

5.85

6.28 6.57

6.89 7.07 7.13 7.23 7.28 7.35 7.39 7.42 7.45 7.49 7.53 7.56 7.59 7.66

7.67 8.09

0

1

2

3

4

5

6

7

8

9

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)

DO(mgO2/L) DO(mgO2/L) Min DO sat

3.60 3.60 3.62 3.70 3.80

4.54 4.95

5.31 5.79 6.08 6.03

6.26 6.41 6.62 6.76 6.85 6.94 7.04 7.13 7.19 7.24 7.36

7.42 7.83

0

1

2

3

4

5

6

7

8

9

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)


57

Figure (5.9): Simulated DO levels for current situation in the upstream reach on June 2013

DO (Downstream) – Results from the simulation for the downstream

reach showed a deflection point at Ras Al-Qilt with significant rise of the

DO concentration (Figures 5.21, 5.22, and 5.23); this was due to the

increased aeration occurring at this location because of its stepped terrain.

However on April, 2013 the DO concentrations did not reached the

saturation because of some temporary pollution sources such as citizens’

recreation visits and cleaning the sheep in the stream which occurs usually

at this time of the year. On May and June, 2013 the simulation curves

reached the saturation curve at Ras Al-Qilt since the pollutions sources

stopped.

For Ras Al-Qilt the simulated DO concentration on April, 2013 was 9.8

mg/L which is 19% higher than the measured value that was 8.22 mg/L. On

May, 2013 the value of the simulated DO concentration was 13.7 mg/L

3.00

3.42 3.87

4.46 4.85

5.81 6.16

6.42 6.74 6.93 6.96 7.06 7.13 7.22 7.29 7.35 7.41 7.48 7.56 7.62 7.68

7.84

8.33 8.78

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)


58

which is 24% higher than the measured value that was 11 mg/L. On June,

2013 the value of simulated DO concentration was 9.4 mg/L which is 9%

higher than the measured value that was 8.61 mg/L.

The saturation DO curve showed slight incremental behavior with

distance for the three time steps, almost 0.3 mg/L. This is due to the fact

that water holds more DO in lower altitudes.

Figure (5.10): Simulated DO levels for current situation in the downstream reach on

April 2013

7.20 7.11 7.02 6.91 6.85 6.74 6.50

8.22 7.89 7.71

7.57 7.36 7.30 7.22 7.18 7.16 7.36 7.53 7.69 7.79 7.87

8.02

8.60 8.89

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)


59

Figure (5.11): Simulated DO levels for current situation in the downstream reach on

May 2013.

Figure (5.12): Simulated DO levels for current situation in the downstream reach

onJune 2013

6.30 6.54

6.79 7.08 7.26 7.59

7.56

8.61

8.52 8.47 8.42 8.34 8.32 8.28 8.26 8.24 8.25 8.26 8.29 8.31 8.34 8.40

8.53 8.82

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)


7.10 7.26 7.43 7.62 7.73 7.95

7.95

10.97 10.39

10.06 9.78

9.33 9.17 8.99 8.88 8.81 8.71

8.63 8.58 8.57 8.56 8.57

8.49 8.77

0

2

4

6

8

10

12

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)


60

5.3.3 Future Scenario (S2)

This case represented the simulation of the same time period as in scenario

one (April, May, and June, 2013) but with different conditions. The

simulated condition here represented one of the management options that

could possibly applied easily to enhance the quality of Al-Qilt stream. This

scenario simulated the DO of the stream after the addition of two stepped

weirs on the upstream reach, at (Al-Bireh and Mukhmas) which increased

the DO concentration levels in this reach of the stream. Al-Bireh and

Mukhmas locations were selected since DO deficits were the highest

between all of the five locations.

The expected DO concentration levels after using artificial aeration by

the stepped weirs with proposed efficiency of 80%; are listed in Table 5.9.

Table (5.9): DO concentrations using weirs in the upstream reach on

April, May and June 2013 Stepped weirs efficiency during April

Location Al-Bireh Mukhmas

DO (Before), mg/L 3 4.7

DO (After), mg/L 7.86 8.03

Stepped weirs efficiency during May


DO (Before), mg/L 2.5 2.5

DO (After), mg/L 7.22 6.9

Stepped weirs efficiency during June


DO (Before), mg/L 3.6 5.2

DO (After), mg/L 7.18 7.87

DO (Improved Upstream) – Values of the simulated DO concentrations at

this reach showed (Figures 5.24, 5.25, and 5.26) relatively high initial

61

concentrations followed by immediate drop due to the deoxygenation and

nitrification.

For Mukhmas the simulated DO concentration on April, 2013 was 7.1

mg/L which is 13% lower than the calculated value that was 8.03 mg/L. On

May, 2013 the simulated DO concentration was 7.2 mg/L which is 4%

higher than the calculated value which was 6.9 mg/L. On June, 2013 the

simulated DO concentration was 6.2 mg/L which is 26% lower than the

calculated value that was 7.87 mg/L.

The saturation DO curve showed slight incremental behavior with distance

for the three time steps, almost 0.3 mg/L. This is due to the fact that water

holds more DO in lower altitudes.

Figure (5.13): Simulated DO levels for future scenario 2 in the upstream reach on April

2013

7.86 7.19

6.67 6.32 6.22 6.47

6.62 6.76 6.96 7.10 7.10 7.17 7.21 7.29 7.35 7.40 7.45 7.52 7.59 7.64 7.69

7.84

8.33 8.77

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)


62

Figure (5.14): Simulated DO levels for future scenario 2 in the upstream reach on May

2013

Figure (5.15): Simulated DO levels for future scenario 2 in the upstream reach on June

2013

7.18

6.26

5.48 4.82 4.57

4.92 5.22

5.51 5.93 6.19 6.13 6.34 6.47

6.66 6.79 6.87 6.95 7.04 7.12 7.18 7.23 7.35

7.42 7.81

0

1

2

3

4

5

6

7

8

9

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)


7.22 6.39 5.89 5.75

5.83 6.42

6.66 6.85 7.06 7.19 7.23 7.30 7.34 7.38 7.42 7.44 7.46 7.49 7.53 7.55 7.58

7.65 7.67

8.07

0

1

2

3

4

5

6

7

8

9

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)


63

5.3.4 Future Scenario (S3)

This case represented the simulation of the same time period as in scenarios

one and two (April, May, and June, 2013) but with different conditions.

The simulated condition here represented a second option of the

management options that could possibly be applied to enhance the quality

of Al-Qilt stream. This scenario simulated the quality of the stream after

constructing WWTP at Qalandia region to treat the raw wastewater flowing

from that area. The water quality parameters and characteristics were

assumed to be the same as the water quality generated from Al-Bireh

WWTP. A comparison between the results from this simulation of this

scenario (S3) and the results from previous scenario (S2) are in (Figures

5.27, 5.28, and 5.29).

Figure (5.16): Simulated DO levels for future scenario 3 in the upstream reach on April

2013

7.86

7.19

6.67 6.32 6.22 6.47 6.62 6.76 6.96

7.10 7.10 7.17 7.21 7.29 7.35 7.40 7.45 7.52 7.59 7.64 7.69 7.84

8.33 8.77

7.22 7.43 7.53 7.66 7.74 7.79 7.85 7.92 7.99 8.04 8.09 8.21

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)

DO(mgO2/L) DO(mgO2/L) Min DO sat DO(mgO2/L)

64

Figure (5.17): Simulated DO levels for future scenario 3 in the upstream reach on May

2013

Figure (5.18): Simulated DO levels for future scenario 3 in the upstream reach on June

2013

7.22

6.39 5.89

5.75 5.83

6.42 6.66 6.85

7.06 7.19 7.23 7.30 7.34 7.38 7.42 7.44 7.46 7.49 7.53 7.55 7.58 7.65

7.67 8.07

7.29 7.42 7.48 7.55 7.58 7.61 7.63 7.66 7.69 7.72 7.75 7.81

0

1

2

3

4

5

6

7

8

9

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km)


7.18

6.26

5.48

4.82 4.57

4.92 5.22

5.51

5.93 6.19 6.13 6.34 6.47 6.66

6.79 6.87 6.95 7.04 7.12 7.18 7.23 7.35

7.42 7.81

6.41 6.74

6.88 7.04 7.13 7.19 7.25 7.31 7.31 7.41 7.45 7.54

0

1

2

3

4

5

6

7

8

9

0 2 4 6 8 10

Dis

solv

ed o

xyg

en (

mg

/L)

Distance (Km) )


65

The conditions in this scenario were the same as in the previous one (S2)

(i.e. the construction of two artificial aeration weirs), but with the addition

of the effects from the WWTP at Qalandia. The modeling rate constants

(e.g. reaeration, deoxygenation, and nitrification) were assumed to be the

same for the flow running form Al-Bireh.

Values for the DO concentration on April, May and June, 2013 were

identical with the values of the DO concentration in the second scenario

(S2), until the flow reached near Mukhmas where the new conditions for

the third scenario apply.

In this scenario, no significant improvements on the DO levels existed. For

April, 2013 only 4.7% of the DO had increased with a raise of 0.37 mg/L.

For May, 2013 only 2.1% of the DO had increased with a raise of 0.16

mg/L. For June, 2013 only 2.6% mg/L of the DO had increased with a raise

of 0.19 mg/L.

66

Chapter Six

Conclusions and Recommendations

6.1 Conclusions

The following are the main conclusions:

1- Pollution levels were higher in the upstream reach (from Al-Bireh

WWTP till the distance of 10.5 km), this was due to the effluent

flowing from Al-Bireh WWTP and from the raw wastewater from

Qalandia region.

2- Highly suspected connection between the pollution sources in the

springs downstream (Fawwar and Ras Al-Qilt) and the raw

wastewater running upstream. Underground connection might exist

in the dry segment of the stream (7 km) between the upstream reach

and downstream reach.

3- Pollution in the downstream reach on April, 2013 was higher than

May and June, 2013 due to the recreation visits to the stream.

4- The stream showed ability of self remediation regarding the DO

concentration, in several locations levels almost reached the

saturation concentration.

5- The values of the key simulation rates (Reaeration, deoxygenation

and nitrification) for Al-Qilt stream exceeded the typical ranges; this

was expected since the used theory was originally derived for large

scale rivers. However, the simulation of Al-Qilt stream which was

considered as small scale stream that showed very reasonable results.

67

6- Raises of the saturation DO curves were noticed in all of the model

simulations range. These raises ranged from 0.28 to 0.45 mg/L, this

was due to the fact that water absorbs more oxygen at lower altitudes

which was the case in this study, since the altitude changing from

elevation of 727 m a.m.s.l to 178 m b.m.s.l. In addition to the effect

of the increased temperature which help the water in absorbing more

oxygen.

7- The suggested stepped weirs in the upstream reach increased the DO

concentrations almost to the saturation levels which indicate that

such low cost solution could improve the stream quality

significantly.

8- The construction of a WWTP to treat the raw wastewater flowing

from Qalandia region, had limited effects on the DO levels with too

much high costs. The maximum raise in DO concentration from this

option was only 0.37 mg/L.

6.2 Recommendations

Based on results of this research, the following are the set of

recommendations:

1- Studying the effect of stepped weirs on the aeration coefficient.

2- The construction of the stepped weirs in the upstream reach, at least

two weirs with 1 ft for each step and with 80% designed efficiency.

3- The major pollution source in the stream was the raw wastewater

flowing from Qalandia region, this source of pollution must be

solved with any possible solution.

68

4- Further studies on water quality modeling on Al-Qilt stream with

longer time periods to cover the wet season are recommended for the

purpose of achieving an integrated water quality management for the

catchment.

69

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76

Appendices

Appendix A

On site measured values from April to June, 2013 at the five locations.

Measured pH values on April, May and June 2013

Measured TDS values on April, May, and June 2013

8.04 8.2 8.3

8.4 8.53

8.3

9.5

8.7 8.8

8.7

9.4 9.5 9.3

9.44 9.6

7

7.5

8

8.5

9

9.5

10


pH

Location

pH on April pH on May pH on June

674 688

319

266 267

678 693

317

254 248

675 680

322 274

236

0

100

200

300

400

500

600

700

800

Al-BirehWWTP


TSD

(m

g/L)

Location

TDS mg/L on April TDS mg/L on May TDS mg/L on June

77

Appendix B

BOD20 values from April to June, 2013 at the five locations

BOD Readings (Round one. Taken 16 April, 2013)

Dates Al-Bireh Murashahat Ras Al-Qilt Fawwar Mukhmas

18-Apr 0 0 0 0 0

19-Apr 0 0 0 15 15

20-Apr 0 15 15 15 15

21-Apr 20 15 15 15 15

22-Apr 20 15 15 35 35

23-Apr 20 15 15 35 35

24-Apr 20 15 15 35 35

25-Apr 20 15 15 35 35

26-Apr 20 15 15 35 35

27-Apr 20 15 20 35 35

28-Apr 20 15 20 35 35

29-Apr 20 15 20 35 35

30-Apr 20 15 20 35 35

1-May 20 15 20 35 35

2-May 20 15 20 35 35

3-May 20 15 20 35 35

4-May 20 15 20 35 55

5-May 20 15 20 35 55

6-May 20 15 20 35 55

7-May 20 15 20 35 55

78

BOD Readings (Round Two Taken 20 May, 2013)

Dates Al-Bireh Murashahat Ras Al-Qilt Fawwar Mukhmas

20-Jun 0 0 0 0 0

21-Jun 5 0 0 5 15

22-Jun 5 5 0 5 15

23-Jun 5 5 0 5 20

24-Jun 5 5 0 5 25

25-Jun 5 5 5 5 25

26-Jun 5 5 5 5 25

27-Jun 5 5 5 5 25

28-Jun 5 5 5 5 30

29-Jun 10 5 5 10 30

30-Jun 10 5 5 10 30

1-Jul 10 5 5 10 35

2-Jul 10 5 5 10 35

3-Jul 10 5 5 10 35

4-Jul 10 5 5 10 35

5-Jul 10 5 5 10 35

6-Jul 15 10 5 10 40

7-Jul 20 10 10 15 45

8-Jul 20 10 10 15 45

9-Jul 25 10 10 15 45

79

BOD Readings (Round Three. Taken 20 June, 2013)

Dates Al-Bireh Murashahat Ras Al Qilt Fawwar Mukhmas

20-Jun 0 0 0 0 0

21-Jun 5 0 0 5 5

22-Jun 5 5 0 5 10

23-Jun 5 5 5 5 10

24-Jun 5 5 5 10 15

25-Jun 10 5 5 10 15

26-Jun 10 5 5 10 20

27-Jun 15 5 5 10 20

28-Jun 15 5 5 10 20

29-Jun 15 5 5 10 25

30-Jun 20 5 5 10 25

1-Jul 20 5 5 10 30

2-Jul 20 5 10 15 30

3-Jul 25 5 10 15 30

4-Jul 25 10 10 15 35

5-Jul 30 10 10 15 35

6-Jul 30 10 10 15 40

7-Jul 35 10 15 15 40

8-Jul 35 10 15 15 45

9-Jul 35 10 15 15 45

BOD5 for Al-Bireh sampling location from April to June, 2013

0

5

10

15

20

25

0 1 2 3 4 5 6

BO

D (

mg\

L)

Days

BOD5 on April BOD5 on May BOD5 on June

80

BOD5 for Murashahat sampling location from April to June, 2013

BOD5 for Ras Al-Qilt sampling location from April to June, 2013

-2

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5 6

BO

D (

mg\

L)

Days


-2

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5 6

BO

D (

mg\

L)

Days


81

BOD5 for Fawwar sampling location from April to June, 2013

BOD5 for Mukhmas sampling location from April to June, 2013

0

5

10

15

20

25

30

35

40

0 1 2 3 4 5 6

BO

D (

mg\

L)

Days


0

5

10

15

20

25

30

35

40

0 1 2 3 4 5 6

BO

D (

mg\

L)

Days


82

Appendix C

Nitrogen values from April to June, 2013 at the five locations

TN for the five sampling locations on April, May, and June 2013

TKN for the five sampling locations on April, May, and June 2013

17.6

41.7 40.2

28.5

38.7 39.3 39.6

34.8

24.9

33.1

22.88

34.93

10.57

18.68

28.88

0

5

10

15

20

25

30

35

40

45


TN (

mg/

L)

Location

Total Nitrogen mg/L on April Total Nitrogen mg/L on May

7.4

11.1

16.4

3.7

11

14.1

10.1

2.2

4.3

7.9

15.18 14.63

3.07

8.08

14.08

0

2

4

6

8

10

12

14

16

18

Al-BirehWWTP


TKN

(m

g/L)

Location

TKN mg/L on April TKN mg/L on May TKN mg/L on June

83

NO-3 for the five sampling locations on April, May, and June 2013

NO-2 for the five sampling locations on April, May, and June 2013

3.6

19

3.4 2.7

8.5

4

11.2

2 3

10

2.3 2.4 1.3 1.2 1.5

0

2

4

6

8

10

12

14

16

18

20

Al-BirehWWTP


NO

3 (m

g/L)

Location

NO2 mg/L on April NO2 mg/L on May NO2 mg/L on June

6.6

11.6

20.4 22.1

19.2 21.2

18.3

30.6

17.6

15.2

5.4

17.9

6.2

9.4

13.3

0

5

10

15

20

25

30

35


NO

3 (m

g/L)

Location

NO3 mg/L on April NO3 mg/L on May NO3 mg/L on June

84

Appendix D

Curves that were used to calculate the Nitrification rate for the five

sampling locations.

Nitrification rate curves for all the five sampling locations on April 2013

y = 0.0351x + 0.3225 R² = 0.9581

y = 0.0354x + 0.4218 R² = 0.9518

y = 0.039x + 0.4643 R² = 0.9518

y = 0.0317x + 0.4647 R² = 0.9499

y = 0.0294x + 0.3501 R² = 0.9518

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10 12 14 16

Time (Days)

Nitrification Rate Curves Mekhmas

Al-Bireh

Murashahat

Ras Al-Qilt

Fawwar

Linear (Mekhmas)

Linear (Al-Bireh)

Linear(Murashahat)Linear (Ras Al-Qilt)

Linear (Fawwar)

85

Nitrification rate curves for all the five sampling locations on May 2013

Nitrification rate curves for all the five sampling locations on June 2013

y = 0.0273x + 0.3852 R² = 0.9129

y = 0.0395x + 0.7635 R² = 0.9943

y = 0.0672x + 0.6169 R² = 0.9581

y = 0.0639x + 0.631 R² = 0.9563

y = 0.0381x + 0.7678 R² = 0.9932

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 2 4 6 8 10 12 14

Time (Days)

Nitrification Rate Curve Mukhmas

Al-Bireh

Murashahat

Ras Al-Qilt

Fawwar

Linear (Mukhmas)

Linear (Al-Bireh)

Linear (Murashahat)

Linear (Ras Al-Qilt)

Linear (Fawwar) (𝑇𝑖𝑚

𝑁𝐵𝑂 )1 3

y = 0.0205x + 0.4579 R² = 0.8365

y = 0.0335x + 0.4846 R² = 0.8696

y = 0.0752x + 0.5865 R² = 0.9626

y = 0.0861x + 0.5521 R² = 0.9685

y = 0.0308x + 0.5713 R² = 0.847

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 5 10 15 20

Time (Days)

Nitrification Rate Curve Mukhmas

Al-Bireh

Murashahat

Ras Al-Qilt

Fawwar

Linear (Mukhmas)

Linear (Al-Bireh)

Linear (Murashahat)

Linear (Ras Al-Qilt)

Linear (Fawwar)

(𝑇𝑖𝑚

𝑁𝐵𝑂 )1 3

الوطنت جبمعت النجبح

كلت الذراسبث العلب

مبه ف وادي القلطالنمذجت نوعت

إعذاد

هبن عبدل أحمذ شزذة

إشزاف

د. عبذ الفتبح حسن

د. سمز شذذ

درجت المبجستز ف هنذست المبه والبئت الحصول على قذمج هذه الألطزوحت إستكمبال لمتطلببث

فلسطن. –بكلت الذراسبث العلب ف جبمعت النجبح الوطنت ف نببلس

2014

‌ب

مبه ف وادي القلطالنمذجت نوعت

إعذاد

هبن عبدل أحمذ شزذة

إشزاف

د. عبذ الفتبح حسن

د. سمز شذذ

الملخص

الفمسطينية لها فإن اىتماما خاصا يجب أن يسمط عمى المياه السطحية محدودة جدا في المناطق نوعية ىهه المصادر المائية المتوفرة. المياه السطحية في حوض القمط تعتبر مصدر مائي ال غنى

يحد اشستخدام الكامل ليها المصدر وىها نوعية المياه في وادي القمط عرضة لممونات عديدة عنو.وحة كان عمى بنا نموهج لنوعية المياه في وادي القمط مع إعتبار . لها التركيز في ىهه األطر الميم

األكسجين المهاب كمعيار أساسي لنوعية المياه في الوادي. لقد تم بنا نموهج لنوعية المياه األكسجين المهاب سموك( وتم محاكات عدة ظروف محتممة لتوقع QUAL2Kwبإستخدام برنامج )

جات في منل إستعمال مدر محتممةالوادي في الظروف الحالية أو في ظروف مدىياتو عمى ومستو أو بنا محطة معالجة لممياه العادمة المتدفقة من منطقة مواقع معينة لتحسين عممية التيوية

التي أدت إلى مميزة لموادي عمى التنقية الهاتيةالعالية و القدرة الالنتائج التي ظيرت أكدت .قمندياكسجين المهاب إلى مستويات وصمت لحد اششباع الكامل وفي بعض المواقع مستويات األإرتفاع

كسجين المهاب حد اششباع إلى أن الجيدة قد تجاوزت المستويات األ التي تتمتع بقدرة عمى التيوية لقد أنبتت النتائج أن الحمول المقترحة المتمنمة في مدرجات التيوية تمنل (.mg/L 11وصمت إلى )

2.5كسجين المهاب من )ه في الوادي وقد رفعت مستويات األحال مناسبا لتحسين نوعية المياmg/L( إلى )7.5 mg/L.) تننير محطة المعالجة القترحة في منطقة قمنديا فإنمن ناحية اخرى

.% في زيادة األكسجين المهاب7.4محدودا بفعالية فقط كان تننيرا

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